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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">注解</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">here</span></a> to download the full example code</p>
</div>
<div class="sphx-glr-example-title section" id="compute-and-reduce-with-tuple-inputs">
<span id="sphx-glr-how-to-work-with-schedules-tuple-inputs-py"></span><h1>Compute and Reduce with Tuple Inputs<a class="headerlink" href="#compute-and-reduce-with-tuple-inputs" title="永久链接至标题">¶</a></h1>
<p><strong>作者</strong>: <a class="reference external" href="https://github.com/ZihengJiang">Ziheng Jiang</a></p>
<p>Often we want to compute multiple outputs with the same shape within
a single loop or perform reduction that involves multiple values like
<code class="code docutils literal notranslate"><span class="pre">argmax</span></code>. These problems can be addressed by tuple inputs.</p>
<p>In this tutorial, we will introduce the usage of tuple inputs in TVM.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">print_function</span>

<span class="kn">import</span> <span class="nn">tvm</span>
<span class="kn">from</span> <span class="nn">tvm</span> <span class="k">import</span> <span class="n">te</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
</pre></div>
</div>
<div class="section" id="describe-batchwise-computation">
<h2>Describe Batchwise Computation<a class="headerlink" href="#describe-batchwise-computation" title="永久链接至标题">¶</a></h2>
<p>For operators which have the same shape, we can put them together as
the inputs of <a class="reference internal" href="../../reference/api/python/te.html#tvm.te.compute" title="tvm.te.compute"><code class="xref any py py-func docutils literal notranslate"><span class="pre">te.compute</span></code></a>, if we want them to be scheduled
together in the next schedule procedure.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;n&quot;</span><span class="p">)</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;m&quot;</span><span class="p">)</span>
<span class="n">A0</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">placeholder</span><span class="p">((</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;A0&quot;</span><span class="p">)</span>
<span class="n">A1</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">placeholder</span><span class="p">((</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;A1&quot;</span><span class="p">)</span>
<span class="n">B0</span><span class="p">,</span> <span class="n">B1</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">compute</span><span class="p">((</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">:</span> <span class="p">(</span><span class="n">A0</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span> <span class="n">A1</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;B&quot;</span><span class="p">)</span>

<span class="c1"># The generated IR code would be:</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">create_schedule</span><span class="p">(</span><span class="n">B0</span><span class="o">.</span><span class="n">op</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">tvm</span><span class="o">.</span><span class="n">lower</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="p">[</span><span class="n">A0</span><span class="p">,</span> <span class="n">A1</span><span class="p">,</span> <span class="n">B0</span><span class="p">,</span> <span class="n">B1</span><span class="p">],</span> <span class="n">simple_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">输出:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primfn(A0_1: handle, A1_1: handle, B_2: handle, B_3: handle) -&gt; ()
  attr = {&quot;from_legacy_te_schedule&quot;: True, &quot;global_symbol&quot;: &quot;main&quot;, &quot;tir.noalias&quot;: True}
  buffers = {B: Buffer(B_4: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type=&quot;auto&quot;),
             B_1: Buffer(B_5: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type=&quot;auto&quot;),
             A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type=&quot;auto&quot;),
             A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_6: int32, stride_7: int32], type=&quot;auto&quot;)}
  buffer_map = {A0_1: A0, A1_1: A1, B_2: B, B_3: B_1} {
  for (i: int32, 0, m) {
    for (j: int32, 0, n) {
      B_4[((i*stride) + (j*stride_1))] = ((float32*)A0_2[((i*stride_4) + (j*stride_5))] + 2f32)
      B_5[((i*stride_2) + (j*stride_3))] = ((float32*)A1_2[((i*stride_6) + (j*stride_7))]*3f32)
    }
  }
}
</pre></div>
</div>
</div>
<div class="section" id="describe-reduction-with-collaborative-inputs">
<span id="reduction-with-tuple-inputs"></span><h2>Describe Reduction with Collaborative Inputs<a class="headerlink" href="#describe-reduction-with-collaborative-inputs" title="永久链接至标题">¶</a></h2>
<p>Sometimes, we require multiple inputs to express some reduction
operators, and the inputs will collaborate together, e.g. <code class="code docutils literal notranslate"><span class="pre">argmax</span></code>.
In the reduction procedure, <code class="code docutils literal notranslate"><span class="pre">argmax</span></code> need to compare the value of
operands, also need to keep the index of operand. It can be expressed
with <code class="xref py py-func docutils literal notranslate"><span class="pre">te.comm_reducer()</span></code> as below:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># x and y are the operands of reduction, both of them is a tuple of index</span>
<span class="c1"># and value.</span>
<span class="k">def</span> <span class="nf">fcombine</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
    <span class="n">lhs</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">tir</span><span class="o">.</span><span class="n">Select</span><span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">rhs</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">tir</span><span class="o">.</span><span class="n">Select</span><span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span>


<span class="c1"># our identity element also need to be a tuple, so `fidentity` accepts</span>
<span class="c1"># two types as inputs.</span>
<span class="k">def</span> <span class="nf">fidentity</span><span class="p">(</span><span class="n">t0</span><span class="p">,</span> <span class="n">t1</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">tvm</span><span class="o">.</span><span class="n">tir</span><span class="o">.</span><span class="n">const</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">t0</span><span class="p">),</span> <span class="n">tvm</span><span class="o">.</span><span class="n">te</span><span class="o">.</span><span class="n">min_value</span><span class="p">(</span><span class="n">t1</span><span class="p">)</span>


<span class="n">argmax</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">comm_reducer</span><span class="p">(</span><span class="n">fcombine</span><span class="p">,</span> <span class="n">fidentity</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;argmax&quot;</span><span class="p">)</span>

<span class="c1"># describe the reduction computation</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;m&quot;</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;n&quot;</span><span class="p">)</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">placeholder</span><span class="p">((</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;idx&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;int32&quot;</span><span class="p">)</span>
<span class="n">val</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">placeholder</span><span class="p">((</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;val&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;int32&quot;</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">reduce_axis</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="s2">&quot;k&quot;</span><span class="p">)</span>
<span class="n">T0</span><span class="p">,</span> <span class="n">T1</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">compute</span><span class="p">((</span><span class="n">m</span><span class="p">,),</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">:</span> <span class="n">argmax</span><span class="p">((</span><span class="n">idx</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">],</span> <span class="n">val</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="n">k</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;T&quot;</span><span class="p">)</span>

<span class="c1"># the generated IR code would be:</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">create_schedule</span><span class="p">(</span><span class="n">T0</span><span class="o">.</span><span class="n">op</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">tvm</span><span class="o">.</span><span class="n">lower</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">T0</span><span class="p">,</span> <span class="n">T1</span><span class="p">],</span> <span class="n">simple_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">输出:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primfn(idx_1: handle, val_1: handle, T_2: handle, T_3: handle) -&gt; ()
  attr = {&quot;from_legacy_te_schedule&quot;: True, &quot;global_symbol&quot;: &quot;main&quot;, &quot;tir.noalias&quot;: True}
  buffers = {T: Buffer(T_4: Pointer(int32), int32, [m: int32], [stride: int32], type=&quot;auto&quot;),
             idx: Buffer(idx_2: Pointer(int32), int32, [m, n: int32], [stride_1: int32, stride_2: int32], type=&quot;auto&quot;),
             T_1: Buffer(T_5: Pointer(int32), int32, [m], [stride_3: int32], type=&quot;auto&quot;),
             val: Buffer(val_2: Pointer(int32), int32, [m, n], [stride_4: int32, stride_5: int32], type=&quot;auto&quot;)}
  buffer_map = {idx_1: idx, val_1: val, T_2: T, T_3: T_1} {
  for (i: int32, 0, m) {
    T_4[(i*stride)] = -1
    T_5[(i*stride_3)] = -2147483648
    for (k: int32, 0, n) {
      T_4[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] &lt;= (int32*)T_5[(i*stride_3)]), (int32*)T_4[(i*stride)], (int32*)idx_2[((i*stride_1) + (k*stride_2))], dtype=int32)
      T_5[(i*stride_3)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] &lt;= (int32*)T_5[(i*stride_3)]), (int32*)T_5[(i*stride_3)], (int32*)val_2[((i*stride_4) + (k*stride_5))], dtype=int32)
    }
  }
}
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">注解</p>
<p>For ones who are not familiar with reduction, please refer to
<a class="reference internal" href="reduction.html#general-reduction"><span class="std std-ref">Define General Commutative Reduction Operation</span></a>.</p>
</div>
</div>
<div class="section" id="schedule-operation-with-tuple-inputs">
<h2>Schedule Operation with Tuple Inputs<a class="headerlink" href="#schedule-operation-with-tuple-inputs" title="永久链接至标题">¶</a></h2>
<p>It is worth mentioning that although you will get multiple outputs
with one batch operation, but they can only be scheduled together
in terms of operation.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;n&quot;</span><span class="p">)</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;m&quot;</span><span class="p">)</span>
<span class="n">A0</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">placeholder</span><span class="p">((</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;A0&quot;</span><span class="p">)</span>
<span class="n">B0</span><span class="p">,</span> <span class="n">B1</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">compute</span><span class="p">((</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">:</span> <span class="p">(</span><span class="n">A0</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span> <span class="n">A0</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;B&quot;</span><span class="p">)</span>
<span class="n">A1</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">placeholder</span><span class="p">((</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;A1&quot;</span><span class="p">)</span>
<span class="n">C</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">compute</span><span class="p">((</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">:</span> <span class="n">A1</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">+</span> <span class="n">B0</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;C&quot;</span><span class="p">)</span>

<span class="n">s</span> <span class="o">=</span> <span class="n">te</span><span class="o">.</span><span class="n">create_schedule</span><span class="p">(</span><span class="n">C</span><span class="o">.</span><span class="n">op</span><span class="p">)</span>
<span class="n">s</span><span class="p">[</span><span class="n">B0</span><span class="p">]</span><span class="o">.</span><span class="n">compute_at</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="n">C</span><span class="p">],</span> <span class="n">C</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="c1"># as you can see in the below generated IR code:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">tvm</span><span class="o">.</span><span class="n">lower</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="p">[</span><span class="n">A0</span><span class="p">,</span> <span class="n">A1</span><span class="p">,</span> <span class="n">C</span><span class="p">],</span> <span class="n">simple_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">输出:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primfn(A0_1: handle, A1_1: handle, C_1: handle) -&gt; ()
  attr = {&quot;from_legacy_te_schedule&quot;: True, &quot;global_symbol&quot;: &quot;main&quot;, &quot;tir.noalias&quot;: True}
  buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type=&quot;auto&quot;),
             A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type=&quot;auto&quot;),
             A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type=&quot;auto&quot;)}
  buffer_map = {A0_1: A0, A1_1: A1, C_1: C} {
  allocate(B.v0: Pointer(global float32), float32, [n]), storage_scope = global;
  allocate(B.v1: Pointer(global float32), float32, [n]), storage_scope = global;
  for (i: int32, 0, m) {
    for (j: int32, 0, n) {
      B.v0[j] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))] + 2f32)
      B.v1[j] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))]*3f32)
    }
    for (j_1: int32, 0, n) {
      C_2[((i*stride) + (j_1*stride_1))] = ((float32*)A1_2[((i*stride_4) + (j_1*stride_5))] + (float32*)B.v0[j_1])
    }
  }
}
</pre></div>
</div>
</div>
<div class="section" id="summary">
<h2>总结<a class="headerlink" href="#summary" title="永久链接至标题">¶</a></h2>
<p>This tutorial introduces the usage of tuple inputs operation.</p>
<ul class="simple">
<li><p>Describe normal batchwise computation.</p></li>
<li><p>Describe reduction operation with tuple inputs.</p></li>
<li><p>Notice that you can only schedule computation in terms of operation instead of tensor.</p></li>
</ul>
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